Tillenius, M., Larsson, E., Lehto, E., & Flyer, N. (2015). A scalable RBF--FD method for atmospheric flow. Journal Of Computational Physics, 298, 406-422. doi:10.1016/j.jcp.2015.06.003
Radial basis function-generated finite difference (RBF--FD) methods have recently been proposed as very interesting for global scale geophysical simulations, and have been shown to outperform established pseudo-spectral and discontinuous Galerkin methods for shallow water test problems. In order ... Show moreRadial basis function-generated finite difference (RBF--FD) methods have recently been proposed as very interesting for global scale geophysical simulations, and have been shown to outperform established pseudo-spectral and discontinuous Galerkin methods for shallow water test problems. In order to be competitive for very large scale simulations, the RBF--FD methods needs to be efficiently implemented for modern multicore based computer architectures. This is a challenging assignment, because the main computational operations are unstructured sparse matrix-vector multiplications, which in general scale poorly on multicore computers due to bandwidth limitations. However, with the task parallel implementation described here we achieve 60-100% of theoretical speedup within a shared memory node, and 80-100% of linear speedup across nodes. We present results for global shallow water benchmark problems with a 30 km resolution. Show less